2(A)-(D): Analysis of pathway activity and intrinsic subtypes

2(A): TCGA BRCA all pathway

## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
## Loading required package: RColorBrewer
## Loading required package: data.table
## Loading required package: mclust
## Package 'mclust' version 5.1
## Type 'citation("mclust")' for citing this R package in publications.
## Loading required package: ggplot2
## Loading required package: gridExtra
## Warning: package 'gridExtra' was built under R version 3.2.4
## Loading required package: devtools
## Loading required package: grid
## Loading required package: cluster
## 
Read 42.8% of 23368 rows
Read 85.6% of 23368 rows
Read 23368 rows and 1120 (of 1120) columns from 0.361 GB file in 00:00:04

2(B): ICBP all pathways

2(C): TCGA, k-means of 4 pathways

2(D): ICBP, k-means of 4 pathways

3(A)-(C): Unsupervised variance analysis confirms the significance of the GFRN phenotypes

Perform Principal Component Analysis in ICBP cell lines

library(ggplot2)
# Remove genes with low variation (filtering)
icbp_f <-icbp[apply(icbp[,1:55]==0,1,mean) < 0.85,]

# Perfrom PCA Analysis 
pca_mat_icbp <- prcomp(t(icbp_f), center=T,scale=T)
rownames(pca_mat_icbp$x)[1:7]<-gsub("X","",rownames(pca_mat_icbp$x)[1:7])

# Plot ICBP PCA Results
plot(pca_mat_icbp,main="ICBP")

#correlate the PCAs with GFRN pathway predictions from ASSIGN
icbp_pca_sig_cors=cor(pca_mat_icbp$x,single_pathway_best_icbp,method="spearman")
heatmap.2(as.matrix(icbp_pca_sig_cors[1:5,1:7]),col=my_palette,margins = c(9,7), trace= "none", main="ICBP PC/Signature Cors")

3(A): Principal Component Analysis in TCGA BRCA

## [1] 23368  1119
## [1] 21307  1119

3(B): Boxplot TCGA Principal Components across clinical subtypes (IHC: ER, PR, HER2)

3(C) Correlate TCGA Principal Components with ASSIGN generated GFRN pathway predictions

library(gplots)
#pdf("~/Dropbox/Multipathway_profiling_paper/Figures/TCGA_PCA_PC5_nokey.pdf ")
pca1_5=pca_mat$x[,1:5]
pca_pathway_cors= cor(pca1_5, single_pathway_best_tcga, method = "spearman")

# Heatmap with signature/PC correlations 
heatmap.2(as.matrix(pca_pathway_cors),col=my_palette,main="Cor between PCs 1-5 and signatures in TCGA  \n BRCA samples", trace = "none", key=T)

4(A)-(E):Survival and growth phenotypes express dichotomous cell survival mechanism

4(A):Heatmap of breast cancer cell lines from Western Blot

4(C)-(E): Gene and protein expression are significantly different between growth and survival phenotypes

## [1] "733 names have been changed"
## [1] "analyzing BAX"
## [1] "BAX data available"

## [1] "analyzing BAK1"
## [1] "analyzing BID"
## [1] "BID data available"

## [1] "analyzing BIM"
## [1] "BIM data available"

## [1] "analyzing BAD"
## [1] "BAD data available"

## [1] "analyzing BIK"
## [1] "analyzing NOXA"
## [1] "analyzing HRK"
## [1] "analyzing PUMA"
## [1] "analyzing BMF"
## [1] "analyzing BCL2"
## [1] "BCL2 data available"

## [1] "analyzing BCLXL"
## [1] "BCLXL data available"

## [1] "analyzing BCLW"
## [1] "analyzing MCL1"
## [1] "analyzing BFL1"

5(A) &(B): Growth factor network phenotypes reflect dichotomous drug response in breast cancer cell lines

5(A): Correlate ICBP GFRN pathway predictions with ICBP drug response and make Heatmap

5(B): Correlate GFRN pathway predictions with drug response in an independent drug assay and make Heatmap

Supplemental Figures 3(A)-(H): Pathway validations codes are in ./ASSIGN folder

Supplemental Figures 4(A)-(G): Boxplot GFRN pathway activity across breast cancer clinical subtypes (IHC)

Supplemental Figures 5(A)-(G): Boxplot GFRN pathway activity across breast cancer intrinsic subtypes (PAM50)

## [1] "517 names have been changed"

Supplemental Figure 6(A)&(B): Boxplot TCGA Principal Components across BRCA intrinsic subtypes (PAM50)

## [1] -0.8238542

Supplemental Figures 7(A)-(D):

This analysis was run on Tue Jun 21 00:02:43 2016